An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis

Andrew Onesimu, J.; Karthikeyan, J

doi:10.22059/jitm.2020.79191

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	An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis
Journal of Information Technology Management
دوره 12، Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.، 2020، صفحه 50-67 اصل مقاله (1.45 M)
نوع مقاله: Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications
شناسه دیجیتال (DOI): 10.22059/jitm.2020.79191
نویسندگان
J. Andrew Onesimu^* ¹؛ J Karthikeyan²
¹Ph.D. Candidate, School of Computer Science and Engineering, Vellore Institute of Technology, India.
²Assistant Professor, School of Information Technology and Engineering, Vellore Institute of Technology, India.
چکیده
In recent privacy has emerged as one of the major concerns of deep learning, since it requires huge amount of personal data. Medical Image Analysis is one of the prominent areas where sensitive data are shared to a third party service provider. In this paper, a secure deep learning scheme called Metamorphosed Learning (MpLe) is proposed to protect the privacy of images in medical image analysis. An augmented convolutional layer and image morphing are two main components of MpLe scheme. Data providers morph the images without privacy information using image morphing component. The human unrecognizable image is then delivered to the service providers who then apply deep learning algorithms on morphed data using augmented convolution layer without any performance penalty. MpLe provides sturdy security and privacy with optimal computational overhead. The proposed scheme is experimented using VGG-16 network on CIFAR dataset. The performance of MpLe is compared with similar works such as GAZELLE and MiniONN and found that the MpLe attracts very less computational and data transmission overhead. MpLe is also analyzed for various adversarial attack and realized that the success rate is as low as . The efficiency of the proposed scheme is proved through experimental and performance analysis.
کلیدواژه‌ها
Deep learning؛ Data privacy؛ Image privacy؛ Medical image analysis؛ Data morphing

مراجع
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An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis